A decade ago, there were just employees at work that cleaned, sorted, and analyzed data but the data scientists we respect now are at the forefront of a relatively new (and coveted) job path. Modern data scientists combine the technical expertise of an analyst with the peculiarity and problem-solving ability of a scientist to form a worthwhile vocation.

Data scientists are always in demand…

According to Accenture’s research, 79 percent of enterprise executives feel that any firm that does not integrate big data into its growth strategy would lose its competitive edge and may go out of business. Even more, 83 percent of businesses surveyed had sought big data projects to become more competitive.

However, getting into the field isn’t always simple. Before attempting to make your imprint in a competitive job market, you must master essential data science skills for 2022.

Many “How to learn data science skills for 2022” courses and publications emphasize basic abilities such as statistics, math, and programming. However, it might be difficult to transform these essential talents into a reality that will help you stand ahead of your competition. Here is a one-of-a-kind list of practical data science skills required that will help you find the perfect job. Depending on your field of specialization, the applicability of these skills may differ.

Let us now dig into what we believe are data science skills in demand:

Writing SQL and developing data pipelines

As a data scientist, knowing how to develop solid SQL queries and planning them on workflow management software like Nintex or Kissflow will make you very valuable. There are two vital reasons for this.

• Companies value data scientists that are capable of doing more than merely modeling data. Full-stack data scientists are in high demand. You’ll be able to better the insights obtained, generate stronger reports, and ultimately make everyone’s jobs easier if you can step in and help build fundamental data pipelines.
• A data scientist’s job is always encountered new challenges every day, there will be times when you require a table or view that does not exist for a model or data science project. Instead of depending on data analysts or data engineers, being able to create robust pipelines while you work on projects will save you time and increase your value.

Data visualization

Data visualization is an important aspect of any data scientist’s job. Analytics specialists who have this talent can transform imposing walls of quantitative and quantitative data into more understandable charts, maps, and graphs. These graphics enable those who lack sophisticated technical skills — such as team leaders and decision-makers — to grasp trends and data patterns quickly and without a lot of explanation. For aspiring data scientists, the ability to visualize data is critical. After all, if you fail to share the data insights you have discovered, you might as well not have found them at all.

Feature engineering

As a potential data scientist, it becomes pivotal for you to have feature engineering as one of the top data science skills to learn.You will need to know how to manipulate data whether you are constructing models, investigating new features to develop, or performing deep dives.  Feature engineering is the process of converting data from one format to another. It is a type of data wrangling that focuses on extracting features from unstructured data. It does not matter whether you use SQL, Java, or Python to manage your data; you should be capable of manipulating your data however you want (within the set parameters).

Regression

If you are a data scientist, your potential employers will expect you to know predictive models, which is one of the top data science skills in demand. Even if it is not something you will do frequently, it is something you will need to master to develop high-performing models. You may come across mission-critical models, which you must be able to handle. Familiarity with data preparation methods, boosted algorithms, hyperparameter tweaking, and model evaluation measures is a must.

Natural Language Processing (NLP)

NLP is a type of artificial intelligence that concentrates on speech and text. NLP, unlike machine learning, is still in its infancy, making it intriguing. It can be used to conduct sentiment analysis to determine how people feel about a company or its product (s). It can be used to keep track of a company’s social media by distinguishing between positive and bad remarks. It is the brains behind chatbots and virtual assistants, as well as text extraction (sifting through documents). Reframing, rapport, swish pattern, diamond technique, fast phobia, core transformation, and anchoring are some vital NLP techniques you should know. Irrespective of your experience in the data science field, you can always earn data scientist certifications to learn about the latest skills in the field. These credentials always keep you updated and assist in keeping pace with the dynamic data science world.

Metric development

Surfacing metrics rely on data to generate the metric and code to calculate and output the metric, data scientists have taken on the duty of metric development. Metric development is a vital data science skill to learn as it involves several factors, such as:

• selecting the appropriate metric for a team or department to employ to track their progress toward their objectives.
• creating the measure, coding it, and setting up a pipeline to track it regularly.
• defining and confirming any assumptions that must be made for the metrics to be valid.

MS Excel

True, Excel may appear to be an unusual — even clumsy — application to put on a list of data science skills for 2022, but it is absolutely important for success. Excel is a key tool for many data scientists, contrary to what you might think from something included in Microsoft’s fairly simple Office suite. The tool comes with its VBA, programming language, including ToolPak, with useful statistical modeling and data analysis tools. Data scientists who have been trained in Excel can employ VBA to create macros, which are pre-recorded commands that make activities like payroll, bookkeeping, and project management much easier for their administrators. PivotTable, a tool that helps data scientists swiftly examine and draw insights from raw data, is also available in Excel. Mastering this odd yet vital tool will undoubtedly benefit you!

The final word…

Working in data science is both professionally and personally gratifying; however, to stay ahead of the competition, you must invest time in developing your skillset. So get down to business! Work immensely on laying the educational groundwork for a successful and long-term career in data science.